Cancer initiates from genetic mutations that disrupt cellular instructions, triggering a cascade of effects across many genes. Understanding this complex transformation is challenging. Traditional single-cell RNA sequencing reveals active genes, but targeting mutations remains difficult. Researchers are now leveraging AI, specifically the GREmLN model, which integrates gene regulatory networks (GRNs) into its framework, moving away from conventional pattern-matching approaches to simulate biological processes more effectively.
GREmLN excels by focusing on plausible gene interactions, utilizing Chebyshev polynomials for efficient computation. Trained on 11 million profiles from the Chan Zuckerberg CELLxGENE platform, it recognizes gene networking and master regulators within cancer cells, aiding in pinpointing critical disease origins and identifying precise therapeutic targets. This model enhances drug development by directing efforts toward master regulators, promising more effective treatments against diseases like cancer. As GREmLN evolves, it aims to broaden its biological context, further advancing predictive biology and immunotherapy initiatives. Researchers can access GREmLN through the virtual cell platform.
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